近红外光谱技术快速测定夏枯草中水溶性浸出物的含量

卢慧娟; 贾灿潮; 曹庆玺; 姬生国

doi:10.13422/j.cnki.syfjx.2016020043

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近红外光谱技术快速测定夏枯草中水溶性浸出物的含量

化学与分析 | 更新时间：2024-01-04

- 近红外光谱技术快速测定夏枯草中水溶性浸出物的含量
- Content Determination of Water-soluble Extract in Prunellae Spica by Near-infrared Spectroscopy
- 中国实验方剂学杂志 2016年22卷第2期页码：43-46
- 作者机构：
- 作者简介：
- 基金信息：
  
  广东省科技计划项目(2009B030801044)
- DOI：10.13422/j.cnki.syfjx.2016020043
  中图分类号： O657.3;R284.1
- 纸质出版日期：2016
- 稿件说明：
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卢慧娟, 贾灿潮, 曹庆玺, 等. 近红外光谱技术快速测定夏枯草中水溶性浸出物的含量[J]. 中国实验方剂学杂志, 2016,22(2):43-46.

LU Hui-juan, JIA Can-chao, CAO Qing-xi, et al. Content Determination of Water-soluble Extract in Prunellae Spica by Near-infrared Spectroscopy[J]. Chinese journal of experimental traditional medical formulae, 2016, 22(2): 43-46.
卢慧娟, 贾灿潮, 曹庆玺, 等. 近红外光谱技术快速测定夏枯草中水溶性浸出物的含量[J]. 中国实验方剂学杂志, 2016,22(2):43-46. DOI： 10.13422/j.cnki.syfjx.2016020043.

LU Hui-juan, JIA Can-chao, CAO Qing-xi, et al. Content Determination of Water-soluble Extract in Prunellae Spica by Near-infrared Spectroscopy[J]. Chinese journal of experimental traditional medical formulae, 2016, 22(2): 43-46. DOI： 10.13422/j.cnki.syfjx.2016020043.

摘要

目的:建立夏枯草中水溶性浸出物含量的近红外光谱定量分析模型。方法:采用热浸法测定180批夏枯草中浸出物的含量;采集样品的近红外光谱数据

利用TQ8.0软件建立浸出物的定量分析模型。结果:所建立的近红外光谱定量分析模型的校正集内部交叉验证相关系数(R2)

校正均方差(RMSEC)和预测均方差(RMSEP)分别为0.9814

0.347

0.378

验证集NIR预测值与热浸法参考值的t检验值为0.653

双侧P=0.517>0.05

说明差异无统计学意义。结论:建立的近红外光谱法测定夏枯草中浸出物的含量测定方法准确、可靠

可用于夏枯草中浸出物的含量测定。

Abstract

Objective:To develop near-infrared spectroscopy (NIR) quantitative analysis model for water-soluble extract in Prunellae Spica. Method:The extract content of Prunellae Spica in 180 batches was determined by hot dipping method. NIRS spectrograms were measured

and the quantitative analysis model for extract was established by TQ8.0 software. Result:The correlation cross-validation coefficients (R2)

root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) of the quantitative calibration model for water extract were 0.9814

0.347 and 0.378 respectively

and the test value of NIR prediction values and the reference values of the validation set was 0.653

bilateral P=0.517>0.05

implying the difference was not significant. Conclusion:The method is steady

accurate and reliable to predict the water extracts content of Prunellae Spica by NIR.

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